Doctoral Dissertation Research: Recovering the Polyvalent Genealogies of Machine Learning, 1948 - 2017

博士论文研究：恢复机器学习的多价谱系，1948 年 - 2017 年

• 批准号: 1829357
• 负责人: Matthew Jones
• 金额: $ 2.62万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829357&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Recovering; Polyvalent

Machine learning techniques currently make "high-stakes" judgments in areas as diverse as criminal justice, credit risk, social welfare, hiring, and congressional redistricting. Such techniques make these decisions using patterns learned from historical social data. Emphasis on prediction rather than the circumstances of dataset creation have led to machine learning systems that preferentially target vulnerable populations for disparately adverse social judgments while making it more difficult for those subject to these decisions to protest unfair treatment. This study explores the limitations of such machine learning systems by tracing how technical and non-technical people, including funding agencies, have historically understood what machine learning systems could and should achieve. Particular care is given to the forms of "learning" valued by researchers during different moments in the 20th century, and to the emergence of theoretical concepts that were constrained and even defined by the capabilities of the available material devices. This work makes visible the efforts of women and men previously omitted in histories of artificial intelligence and machine learning, and develops a quantitative method to document how the innovations of a discipline are contingent upon interdisciplinary and transdisciplinary research networks. Finally, this project traces how the allocation of resources to particular research communities spurred scientific innovation in adjacent and seemingly unrelated academic research fields in the physical and social sciences. In this sense, the discipline of machine learning provides a useful case study for modeling the propagation of ideas across different subfields.Both qualitative and quantitative historical research is employed. First, nine university and government archives are perused to reconstruct the institutional organizations, interpersonal research networks, and material computing devices available to machine learning and artificial intelligence researchers. Second, an investigation is conducted using a novel combination of topic modeling and word embeddings on a corpus of millions of full-text Association of Computing Machinery articles from 1950 to 2017 to trace how discursive influence propagates across disparate sub-disciplines. Four research products are generated: (1) a technical machine learning publication detailing the novel method used to analyze the article corpus, (2) a history of science article tracing the early history of machine learning, (3) a general audience "think piece" discussing the policy and ethical implications of contemporary machine learning research, (4) and the public release of the project code and the subsequent statistics generated from the article corpus. Digital copies of salient archive records discovered during this research study will be made freely available via Columbia University's Digital Repository, insofar as this is possible given the copyright and access restrictions of holding institutions. Archive materials collected and computational study of the article corpus will be used in the co-PI's doctoral dissertation exploring how machine learning has been used to classify individuals, imagine communities, and legislate forms of social and political evidence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器学习技术目前在刑事司法、信用风险、社会福利、招聘和国会选区重划等各种领域做出“高风险”判断。这种技术使用从历史社会数据中学习到的模式做出这些决策。强调预测而不是数据集创建的环境导致机器学习系统优先针对弱势群体进行不同的不利社会判断，同时使受这些决定影响的人更难以抗议不公平待遇。本研究通过追踪技术人员和非技术人员（包括资助机构）在历史上如何理解机器学习系统可以和应该实现的目标，探索了这种机器学习系统的局限性。本书特别关注了20世纪不同时期研究人员所重视的“学习”形式，以及受现有物质设备能力限制甚至定义的理论概念的出现。这项工作让人们看到了以前在人工智能和机器学习历史中被忽略的女性和男性的努力，并开发了一种定量方法来记录一个学科的创新如何依赖于跨学科和跨学科的研究网络。最后，本项目追溯了资源分配到特定研究群体如何刺激物理科学和社会科学中相邻且看似不相关的学术研究领域的科学创新。从这个意义上说，机器学习学科提供了一个有用的案例研究，可以对不同子领域的思想传播进行建模。采用定性和定量的历史研究。首先，我们仔细阅读了九所大学和政府的档案，以重建机构组织、人际研究网络和可供机器学习和人工智能研究人员使用的材料计算设备。其次，使用主题建模和词嵌入的新组合对1950年至2017年数百万篇计算机协会全文文章的语料库进行了调查，以追踪话语影响如何在不同的子学科之间传播。生成了四个研究产品：(1)技术机器学习出版物，详细介绍了用于分析文章语料库的新方法，(2)追溯机器学习早期历史的科学史文章，(3)讨论当代机器学习研究的政策和伦理影响的一般受众“思考文章”，(4)以及公开发布项目代码和从文章语料库生成的后续统计数据。在这项研究中发现的重要档案记录的数字副本将通过哥伦比亚大学的数字存储库免费提供，只要考虑到持有机构的版权和访问限制。收集的档案材料和文章语料库的计算研究将用于共同pi的博士论文，探索如何使用机器学习来对个人进行分类，想象社区，以及立法形式的社会和政治证据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。